ABSTRACT Obsessions and compulsions affect ~30% of the population; when they become severe they lead to a diagnosis of obsessive-compulsive disorder (OCD), which affects one person in 40. Available treatments, including pharmacotherapy with the selective serotonin reuptake inhibitor (SSRI) antidepressants and specialized psychotherapy, are of benefit to many, but individualized response is heterogeneous and unpredictable. Understanding the brain mechanisms of therapeutic change is urgently needed and may guide the development of new interventions. Ultimately, the ability to predict who will respond to a particular treatment would be a major theoretical and clinical advance, would accelerate deployment of effective treatment, and would thereby greatly reduce morbidity. Early studies using perfusion imaging have hinted that baseline neural markers can predict response to pharmacotherapy. However, these studies have not harnessed modern network-focused analytic methods and have not yielded mechanistic insight or clinical utility. Neuropsychiatric disorders are hypothesized to derive from altered functional brain networks. Resting-state functional connectivity MRI (rs-fcMRI) has emerged as a powerful tool to characterize functional network architecture in humans. We propose to use rs-fcMRI, employing state-of-the-art methodologies pioneered by the Human Connectome Project, to map the relationship between functional neural networks and treatment response in OCD. Specifically, we aim to characterize rs-fcMRI connectivity profiles that map onto treatment-associated changes and that predict response. The feasibility of this project is supported by our pilot data. We focus on first-line SSRI pharmacotherapy with fluoxetine as a tractable first step; future studies will incorporate other treatment modalities, including psychotherapy. We propose an innovative clinical design that dissociates treatment from time effects, which is a major challenge in studies of treatment mechanism. 80 medication-free OCD subjects will be randomized 1:1 to receive fluoxetine treatment starting either immediately or after a 6-week placebo lead-in phase. OCD subjects will undergo imaging at baseline and at 6, 12 and 18 weeks. All subjects will be pooled to identify correlates of symptom improvement. The immediate and delayed treatment groups will be contrasted to dissociate treatment-induced neural changes from the non-specific effects of therapeutic contact (i.e. placebo). 40 matched controls will be scanned once and compared with OCD subjects at baseline, prior to pharmacotherapy, to characterize connectivity alterations in the unmedicated state. Neuroimaging data will be analyzed using whole-brain general linear models (GLMs), including between-group and longitudinal effects to isolate effects of time, effects of drug exposure per se, and correlates of clinical improvement. Baseline imaging data will be examined for treatment response prediction, using both a GLM-based regression and via a recently optimized individual classifier, trained on 75% of the sample and then tested on the remaining 25%. This study will yield a rich multi-modal neuroimaging dataset elucidating the neural correlates of OCD symptomatology and of treatment response. If successful, we will identify network targets for novel treatments and take a major step towards the goal of developing predictive measures in the service of precision medicine in psychiatry.